contributor author | Zhong Tang | |
contributor author | Brenda McCabe | |
date accessioned | 2017-05-08T21:13:21Z | |
date available | 2017-05-08T21:13:21Z | |
date copyright | July 2007 | |
date issued | 2007 | |
identifier other | %28asce%290887-3801%282007%2921%3A4%28265%29.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl/handle/yetl/43326 | |
description abstract | A Bayesian belief network (BBN) can be a powerful tool in decision making processes. Development of a BBN requires data or expert knowledge to assist in determining the structure and probabilistic parameters in the model. As data are seldom available in the engineering decision making domain, a major barrier in using domain experts is that they are often required to supply a huge and intractable number of probabilities. Techniques for using fractional data to develop complete conditional probability tables were examined. The results showed good predictability of the missing data in a linear domain by the piecewise representation method. By using piecewise representation, the number of probabilities to be elicited for a binary child node with | |
publisher | American Society of Civil Engineers | |
title | Developing Complete Conditional Probability Tables from Fractional Data for Bayesian Belief Networks | |
type | Journal Paper | |
journal volume | 21 | |
journal issue | 4 | |
journal title | Journal of Computing in Civil Engineering | |
identifier doi | 10.1061/(ASCE)0887-3801(2007)21:4(265) | |
tree | Journal of Computing in Civil Engineering:;2007:;Volume ( 021 ):;issue: 004 | |
contenttype | Fulltext | |